ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2019
Practical algorithms for amyloid β probability in subjective or mild cognitive impairment.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Development of Simple Risk Scores for Prediction of Brain β-Amyloid and Tau Status in Older Adults With Mild Cognitive Impairment: A Machine Learning Approach.The journals of gerontology. Series B, Psychological sciences and social sciences · 2025Article
- Predicting amyloid beta accumulation in cognitively unimpaired older adults: Cognitive assessments provide no additional utility beyond demographic and genetic factors.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Development and assessment of algorithms for predicting brain amyloid positivity in a population without dementia.Alzheimer's research & therapy · 2024Article
- Stricker Learning Span criterion validity: a remote self-administered multi-device compatible digital word list memory measure shows similar ability to differentiate amyloid and tau PET-defined biomarker groups as in-person Auditory Verbal Learning Test.Journal of the International Neuropsychological Society : JINS · 2024Article
- Predicting amyloid-beta pathology in the general population.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Predicting Amyloid Positivity in Cognitively Unimpaired Older Adults: A Machine Learning Approach Using A4 Data.Neurology · 2022Article
- Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Review
- Detecting Alzheimer's disease biomarkers with a brief tablet-based cognitive battery: sensitivity to Aβ and tau PET.Alzheimer's research & therapy · 2021Article
- Detection of β-amyloid positivity in Alzheimer's Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers.Brain communications · 2021Article
- Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
- Predicting amyloid status using self-report information from an online research and recruitment registry: The Brain Health Registry.Alzheimer's & dementia (Amsterdam, Netherlands) · 2020Article
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Authors and funding
9 authors.
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Abstract
introductionPractical algorithms predicting the probability of amyloid pathology among patients with subjective cognitive decline or mild cognitive impairment may help clinical decisions regarding confirmatory biomarker testing for Alzheimer's disease.
methodsAlgorithm feature selection was conducted with Alzheimer's Disease Neuroimaging Initiative and Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing data. Probability algorithms were developed in Alzheimer's Disease Neuroimaging Initiative using nested cross-validation accompanied by stratified subsampling to obtain 1000 internally validated decision trees. Semi-independent validation was conducted using Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing. Independent external validation was conducted in the population-based Mayo Clinic Study of Aging.
resultsTwo algorithms were developed using age and normalized immediate recall z-scores, with or without apolipoprotein E ε4 carrier status. Both algorithms had robust performance across data sets and when substituting different recall memory tests. DISCUSSION: The statistical framework resulted in robust probability estimation. Application of these algorithms may assist in clinical decision-making for further testing to diagnose amyloid pathology.
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